This study assesses the impact of the novel coronavirus disease (COVID-19) cases on the Japanese stock market. As of October 30, 2020, the cumulative number of cases in Japan has reached over one hundred thousand. COVID-19 has significantly affected both the lifestyle and the economy in Japan. First, this study develops composite stock indices by industry sector and prefecture, taking into consideration the effects of the increase in infections on industries and firms in the core prefectures. Second, this study investigates the dynamic conditional correlations between the composite stock index returns and the increment in COVID-19 cases using dynamic conditional correlation multivariate GARCH models. Finally, it can contribute to financial research in terms of coexistence of regional business economies with COVID-19.This field report explores how nonlocal grassroots organizations provided effective and quick responses during the initial stage of the COVID-19 outbreak in Wuhan and surrounding regions. Despite the lack of resources and local connections, they were able to overcome administrative failures and provide quick responses to the crisis. Built on a researcher-practitioner collaborative action research project, three strategies facilitating grassroots organizations' quick and effective responses are analyzed and discussed putting pandemic relief as the strategic priority of their organizations, leveraging social media platforms to scale up existing organizational networks and foster cross-sector collaboration, and effective online trust-building. As COVID-19 unprecedently pushes nonprofits to transform how they deliver services and engage stakeholders, these findings have important policy and theoretical implications for an expanded view of how nonprofits may engage in disaster responses and how public and private funders may shift their funding strategies to cultivate such capacities of grassroots nonprofits.Currently, the impacts of Covid-19 are receiving significant global attention. This also applies to the extractive industries, where this global crisis is directing the gaze of policymakers, donors and academics alike. Covid-19 is seen as having far-reaching and disruptive consequences, especially in the case of artisanal and small-scale mining. While the authors consider this attention important, their work on artisanal and small-scale mining in Ghana - and West Africa more broadly - reveals that for many miners, Covid-19 is 'just' another interruption to their lives and lifeworlds which are chronically affected by interruptions of different scales, magnitudes and temporalities. As anthropologists have shown, foregrounding this structural condition - which is emblematic for the lives of many people, especially in the Global South - is key to questioning, understanding and contextualizing the current moment of 'global' crisis and must be an element of any policy and research emerging from it.During the COVID-19 crisis, digital informal learning is important for students' academic engagement. Although scholars have highlighted the importance of students' digital competence in improving digital informal learning (DIL), the mediating role of DIL between digital competence and academic engagement has remained ambiguous. The purpose of this study is to investigate the relationship between students' digital competence and their academic engagement with the mediating role of DIL in the higher education context. This study used a descriptive correlational design, and the data were analyzed using structural equation modelling (SEM). The study sample included 308 students from Shiraz University, Iran. The results showed that digital competence positively and significantly correlated with students' DIL and their academic engagement. Furthermore, DIL, as the mediator variable, was found to mediate the relationship between students' digital competence and their academic engagement. Since higher education institutions have a key role in improving students' academic engagement, particularly in the COVID-19 pandemic, academic administrators should pay more attention to students' digital competencies and provide them with efficient and user-friendly DIL platforms that can increase their academic engagement.Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. https://www.selleckchem.com/products/PIK-75-Hydrochloride.html COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.